Increases in access may be accompanied by changes in the risk perceptions of water sources that are effectively used by households. To explore these changes in subjective quality, we constructed a risk score for each household. The score used the responses to the questions “How likely is a person to get sick from drinking the water from […]?” for each water source that the household used. The responses were on a scale of no chance, some chance, and high chance. The risk score measured the risk perception of the best water that the household used. For example, a household that used a poliduct39 and a well would get the score of the source they believed was the safest; while a household that used a well and a household tap would get the score for the riskiness of the tap water system. Sources that are in the same 'quality' category received the score of the safest because a rational decision maker would select the safest source as a drinking source. If the sources were in different 'quality' categories, they received the score of the better-quality source. The purpose was not to have to rank similar sources arbitrarily. In the ideal situation of improvement and trust in the water services provided, we would see the risk score at "no chance" for all households, in that every household had a source which they thought was safe to drink. Improvements in the risk perception of water sources are reflected as decreases in the risk score.40
Table 59 presents the impact estimates from the linear probability models for the probability of responding
“no chance” in panel A and responding “high chance” in panel B. The results confirm that treatment households were 15 percentage points more likely to report that there was no chance of getting sick from
39 A poliduct is a long system of hoses that brings water to the household from a natural water source.
40 See Annex 6: Risk Score Allocation Example for a hypothetical situation mapping out risk scores for several households.
117 consuming the water from their best source after the projects were implemented. Treatment households, similarly were 17 percentage points less likely to responding there was a high chance of getting sick from drinking the water from the system.
We estimated the impact on this risk measures for the sample of households that lives in communities with water systems. For these households, we have their perceptions of the likelihood of getting sick from consuming water from these systems even if they are not connected to the systems. The negative coefficients in panel B of Table 59 confirms that households living in treatment segments perceived the water projects as a safe source of drinking water.
Next, we present the impact estimates for the level of satisfaction with the water system used. This is a subjective measure of the quality of the water system in the community both from a health and reliability standpoint. The level of satisfaction was given by the household response to the question. "How satisfied are you with the tap-water service?" on a scale from zero to three, with three being very satisfied and zero being very unsatisfied. As seen in Table 60, treatment households were more likely to report being very satisfied with the water systems after the water projects were implemented in comparison to similar households in segments where no water projects were implemented. The impact comes from a lower probability of reporting their dissatisfaction and higher probability of reporting that they are very satisfied with the systems (13 percent in column 3).
118
TABLE 59 RISK PERCEPTIONS OF BEST WATER SOURCE USED
(1) (2) (3) (4) (5) (6) (7)
Inside Project Area # Post Period 0.25
[0.037]***
Inside Project Area-ITT in Post Period 0.22
[0.053]***
Inside Project Area # Post Period -0.22
[0.041]***
Inside Project Area-ITT in Post Period -0.25
[0.050]***
Std. errors are clustered at the census segment level.
All equations include year fixed effects. Equations are DID, in (1)-(3) the treatment is defined as living in a treatment assigned segment, (4)-(5) treatment is defined as living inside the project area within the matched pairs, (6)-(7) treatment is defined as having reported being beneficiary of the WASH project within the matched pairs. Equations (4)-(7) control for initial treatment assignment.
Equation (2) includes household fixed effects. Pair dummies indicated in the table are based in on nearest neighbor matching propensity score matching based on 2007 census segment data.
IV estimates in columns (5) and (7) use the census segment treatment assignment to instrument for indicators for being in a project area in 2012-2013 (5);
and the households reporting being a beneficiary of the WASH projects from MCC in (7)
^IV estimates partial out the indicators for pairs to compute the std. errors of the coefficients of interest. We report the K-P rk Wald F statistic following the results in Stock and Yogo (2005)
* p<0.10, ** p<0.05, *** p<0.01
119 TABLE 60 RISK PERCEPTIONS OF TAP WATER SYSTEMS
(1) (2) (3) (4) (5) (6) (7)
Inside Project Area # Post Period 0.22
[0.047]***
Inside Project Area-ITT in Post Period 0.18
[0.067]***
Inside Project Area # Post Period -0.15
[0.051]***
Inside Project Area-ITT in Post Period -0.19
[0.059]***
Std. errors are clustered at the census segment level.
All equations include year fixed effects. Equations are DID, in (1)-(3) the treatment is defined as living in a treatment assigned segment, (4)-(5) treatment is defined as living inside the project area within the matched pairs, (6)-(7) treatment is defined as having reported being beneficiary of the WASH project within the matched pairs. Equations (4)-(7) control for initial treatment assignment.
Equation (2) includes household fixed effects. Pair dummies indicated in the table are based in on nearest neighbor matching propensity score matching based on 2007 census segment data.
IV estimates in columns (5) and (7) use the census segment treatment assignment to instrument for indicators for being in a project area in 2012-2013 (5);
and the households reporting being a beneficiary of the WASH projects from MCC in (7)
^IV estimates partial out the indicators for pairs to compute the std. errors of the coefficients of interest. We report the K-P rk Wald F statistic following the results in Stock and Yogo (2005)
* p<0.10, ** p<0.05, *** p<0.01
120
TABLE 61 SATISFACTION/SUBJECTIVE QUALITY OF THE WATER SYSTEM
(1) (2) (3) (4) (5) (6) (7)
Inside Project Area # Post Period -0.086
[0.035]**
Inside Project Area-ITT in Post Period -0.067
[0.041]
Inside Project Area # Post Period 0.16
[0.035]***
Inside Project Area-ITT in Post Period 0.17
[0.049]***
Std. errors are clustered at the census segment level.
All equations include year fixed effects. Equations are DID, in (1)-(3) the treatment is defined as living in a treatment assigned segment, (4)-(5) treatment is defined as living inside the project area within the matched pairs, (6)-(7) treatment is defined as having reported being beneficiary of the WASH project within the matched pairs. Equations (4)-(7) control for initial treatment assignment.
Equation (2) includes household fixed effects. Pair dummies indicated in the table are based in on nearest neighbor matching propensity score matching based on 2007 census segment data.
IV estimates in columns (5) and (7) use the census segment treatment assignment to instrument for indicators for being in a project area in 2012-2013 (5); and the households reporting being a beneficiary of the WASH projects from MCC in (7)
^IV estimates partial out the indicators for pairs to compute the std. errors of the coefficients of interest. We report the K-P rk Wald F statistic following the results in Stock and Yogo (2005)
* p<0.10, ** p<0.05, *** p<0.01
121
G
ENDER ANDS
OCIO-E
CONOMICH
ETEROGENEITY INA
CCESSWe explored heterogeneity of effects across gender of the head of household and across socio-economic status using the expenditure quintile at baseline to disaggregate the impact estimates on access and reliability presented above.
Figure 22 and Figure 23 show that male headed households and female headed households had similar on access to sanitation and tap water. The impact for male vs. female headed household is not statistically different from each other as evidence by the triple difference estimate in the figures.
FIGURE 22 GENDER IN UPTAKE: PROBABILITY OF HAVING AN IMPROVED LATRINE
FIGURE 23 GENDER IN UPTAKE: PROBABILITY OF HAVING A HOUSEHOLD TAP
In the case of socio-economic differences in access, the results imply that households in higher quintiles benefitted more (with DID estimates slightly larger) but not significantly different as shown by the triple difference estimates in Figure 24 for sanitation and Figure 25 for access to tap water.
Treatment # Post Period # Male
Treatment # Post Period # Female
Has own sanitation service
-.04 -.02 0 .02 .04 .06
DID Triple DID
Treatment # Post Period # Male
Treatment # Post Period # Female
Has a acces to HH tap connection
-.1 0 .1 .2 .3
DID Triple DID
122
FIGURE 24 SOCIO-ECONOMIC IN UPTAKE: PROBABILITY OF HAVING AN IMPROVED LATRINE
FIGURE 25 SOCIO-ECONOMIC IN UPTAKE: PROBABILITY OF HAVING A HOUSEHOLD TAP Treatment # Post Period # Q1
Treatment # Post Period # Q2
Treatment # Post Period # Q3
Treatment # Post Period # Q4
Treatment # Post Period # Q5
Has own sanitation service
-.1 -.05 0 .05 .1
DID Triple DID
Treatment # Post Period # Q1
Treatment # Post Period # Q2
Treatment # Post Period # Q3
Treatment # Post Period # Q4
Treatment # Post Period # Q5
Has a acces to HH tap connection
-.1 0 .1 .2 .3 .4
DID Triple DID
123